The Global Positioning System (GPS) is a satellite navigation system developed by the United States in the 1980s. When appropriately used, GPS can provide accurate positioning and timing information for both military and civil applications. The central function of a GPS navigation algorithm is to estimate the user position and any required parameters based on noisy observations of satellite signals. A typical GPS receiver comprises three blocks: a radio-frequency ("RF") front-end, a demodulator, and a navigation processor. The RF front-end receives radio signals from satellites and converts them into a suitable form for the demodulator. The demodulator retrieves navigation data from the signals and performs phase measurements to calculate pseudo-ranges between satellites and the receiver. The navigation processor, a key component in the receiver, then estimates user position with this information.
A conventional navigation processor uses a short burst in navigation data to predict the position of all visible satellites and then chooses at least four satellites as data sources for use by a functional "single-point solver" block, for example as characterized in Global Positioning System: Theory and Application, B. W. Parkinson et al., AIAA, 1996. The single point solver, then, determines the user's position. After that, a self-regulated time-varying-gain filter further smoothes these position determinations.
The GPS receiver's determination of the user's position is based on measurements of phases of pseudo-random codes, as received in the GPS data streams, to estimate distances between satellites and the user. These user position estimates, however, are merely estimates and not precisely accurate. To improve the accuracy, an averaging process, such as time-varying-gain filtering, is used. Such an averaging process is based on the premise that a longer averaging period will yield a smaller averaging variance. Time-varying-gain filtering provides an on-line optimal processing and has been used extensively in navigation applications. For further information concerning such data processing, reference may be made to the above-mentioned article, and to Introduction to Random Signals and Applied Kalman Filtering, by R. G. Brown et al., John Wiley & Sons, 1992, and to Kalman Filtering, Theory and Application, H. W. Sorenson, IEEE Press, 1985.
Kalman filtering is widely used largely due to its ability to optimally minimize the mean square error of position estimates. Conventional use of the Kalman filtering process suffers, however, from sluggish response to dynamic maneuvers if the receiver does not provide information on user dynamics. To avoid this shortcoming, conventional applications have used supplementary devices, such as speedometers or an internal inertial systems, to provide such information.
GPS receivers can also utilize Doppler shift on the carrier to acquire user dynamics. The most commonly used method is the delta range that is an approximate measurement of the user velocity. However, this only improves tracking for low dynamic users. For highly dynamic applications, a better velocity profile can be obtained by carrier aiding. The method of carrier aiding performs a continuously-running integration of Doppler shift instead of a short interval integration, which is done in delta ranging. These measurements, unfortunately, are all very noisy and cycle slips further degrade communication.
GPS-type receivers are used in a wide variety of lightweight, low-power, portable, and/or mobile electronic devices. Many marketplace applications demand that such devices be implemented with higher degrees of navigation and tracking accuracy. Unfortunately, a major design issue in such receivers is the inability to achieve significantly improved tracking ability without degrading steady-state position accuracy.
Accordingly, a need exists for navigation arrangements and methods that address the demands of the marketplace and overcome the above-mentioned problems.